Cohen et al_CLIA CRC_Clinical analysis 11022024
library(swimplot) library(pheatmap) library(reshape2) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(broom) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(survival) library(janitor) library(openxlsx) library(writexl) library(rms) library(DT)
#ctDNA Detection rate by Stage and Window
#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Anytime post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#ctDNA MRD Detection rate Stage I/II vs III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("I", "II"), "I/II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
NEGATIVE POSITIVE
I/II 162 15
III 172 56print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 16.741, df = 1, p-value = 4.285e-05#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data_subset <- circ_data %>%
select(
Age,
Gender,
PrimSite,
pT,
pN,
Stage,
Grade,
NAC,
ACT,
MSI,
BRAF.V600E,
RAS,
DFS.Event,
OS.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
pN = factor(pN, levels = c("N0", "N1-N2")),
Stage = factor(Stage, levels = c("I","II", "III")),
Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
MSI = factor(MSI, levels = c("MSS", "MSI-High")),
BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 7951 |
|---|---|
| Age | 61 (13 - 91) |
| Gender | |
| Male | 407 (51%) |
| Female | 388 (49%) |
| PrimSite | |
| Right-sided colon | 411 (52%) |
| Left-sided colon | 384 (48%) |
| pT | |
| T0 | 3 (0.4%) |
| T1-T2 | 133 (17%) |
| T3-T4 | 654 (83%) |
| Unknown | 5 |
| pN | |
| N0 | 310 (39%) |
| N1-N2 | 482 (61%) |
| Unknown | 3 |
| Stage | |
| I | 47 (5.9%) |
| II | 262 (33%) |
| III | 486 (61%) |
| Grade | |
| G1 | 84 (11%) |
| G2 | 559 (72%) |
| G3 | 127 (16%) |
| GX | 4 (0.5%) |
| Unknown | 21 |
| NAC | |
| Neoadjuvant Chemotherapy | 0 (0%) |
| Upfront Surgery | 795 (100%) |
| ACT | |
| Adjuvant Chemotherapy | 522 (66%) |
| Observation | 273 (34%) |
| MSI | |
| MSS | 664 (84%) |
| MSI-High | 131 (16%) |
| BRAF.V600E | |
| BRAF WT | 699 (88%) |
| BRAF V600E | 96 (12%) |
| RAS | |
| RAS WT | 459 (58%) |
| RAS Mut | 336 (42%) |
| DFS.Event | |
| Recurrence | 141 (18%) |
| No Recurrence | 654 (82%) |
| OS.months | 27 (0 - 103) |
| 1 Median (Range); n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE,
strip_md_bold = TRUE)Warning: The `strip_md_bold` argument of `as_flex_table()` is deprecated as of gtsummary 1.6.0.fit1Characteristic | N = 7951 |
|---|---|
Age | 61 (13 - 91) |
Gender | |
Male | 407 (51%) |
Female | 388 (49%) |
PrimSite | |
Right-sided colon | 411 (52%) |
Left-sided colon | 384 (48%) |
pT | |
T0 | 3 (0.4%) |
T1-T2 | 133 (17%) |
T3-T4 | 654 (83%) |
Unknown | 5 |
pN | |
N0 | 310 (39%) |
N1-N2 | 482 (61%) |
Unknown | 3 |
Stage | |
I | 47 (5.9%) |
II | 262 (33%) |
III | 486 (61%) |
Grade | |
G1 | 84 (11%) |
G2 | 559 (72%) |
G3 | 127 (16%) |
GX | 4 (0.5%) |
Unknown | 21 |
NAC | |
Neoadjuvant Chemotherapy | 0 (0%) |
Upfront Surgery | 795 (100%) |
ACT | |
Adjuvant Chemotherapy | 522 (66%) |
Observation | 273 (34%) |
MSI | |
MSS | 664 (84%) |
MSI-High | 131 (16%) |
BRAF.V600E | |
BRAF WT | 699 (88%) |
BRAF V600E | 96 (12%) |
RAS | |
RAS WT | 459 (58%) |
RAS Mut | 336 (42%) |
DFS.Event | |
Recurrence | 141 (18%) |
No Recurrence | 654 (82%) |
OS.months | 27 (0 - 103) |
1Median (Range); n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#Heatmap with Clinical & Genomics Factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)
ha <- HeatmapAnnotation(
Stage = circ_data$Stage,
Gender = circ_data$Gender,
PrimSite = circ_data$PrimSite,
pT = circ_data$pT,
pN = circ_data$pN,
Grade = circ_data$Grade,
ACT = circ_data$ACT,
MSI = circ_data$MSI,
BRAF.V600E = circ_data$BRAF.V600E,
RAS = circ_data$RAS,
ctDNA.MRD = circ_data$ctDNA.MRD,
ctDNA.Surveillance = circ_data$ctDNA.Surveillance,
ctDNA.anytime = circ_data$ctDNA.anytime,
DFS.Event = circ_data$DFS.Event,
col = list(Stage = c("I" = "seagreen1", "II" = "orange", "III" = "purple"),
Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
PrimSite = c("Right-sided colon" = "brown", "Left-sided colon" ="darkgreen"),
pT = c("T0" = "khaki","T1-T2" = "khaki", "T3-T4" ="brown2"),
pN = c("N0" = "cornflowerblue", "N1-N2" ="orange2"),
Grade = c("GX" = "grey","G1" = "coral", "G2" ="darkgreen", "G3" = "yellow3"),
ACT = c("TRUE" = "#C1211A", "FALSE" ="#008BCE"),
MSI = c("MSS" = "grey", "MSI-High" ="black"),
BRAF.V600E = c("WT" = "grey", "MUT" ="black"),
RAS = c("WT" = "grey", "MUT" ="black"),
ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.Surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
DFS.Event = c("TRUE" = "red3", "FALSE" ="blue")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()#DFS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 328 29 NA NA NA
ctDNA.MRD=POSITIVE 62 36 11.3 8.67 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 110.000 25.000 0.903 0.019 0.859 0.935
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 12.0000 36.0000 0.3608 0.0669 0.2332 0.4898 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.2879 9.8539 0.2517 9.088 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.854 0.1015 6.016 16.14
Concordance= 0.742 (se = 0.029 )
Likelihood ratio test= 74.36 on 1 df, p=<2e-16
Wald test = 82.6 on 1 df, p=<2e-16
Score (logrank) test = 124 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.85 (6.02-16.14); p = 0"#ctDNA sample positive in the MRD Window - 2-10 week intervals
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD,
breaks = c(2, 4, 6, 8, 10),
right = FALSE,
labels = c("2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
group_by(ctDNA_bucket) %>%
summarise(
n_total = n(), # Total number of patients in the bucket
n_positive = sum(biomarker_status == "POSITIVE"), # Number of positive cases
n_negative = sum(biomarker_status == "NEGATIVE"), # Number of negative cases
percentage_positive = mean(biomarker_status == "POSITIVE") * 100, # Positivity rate
percentage_negative = mean(biomarker_status == "NEGATIVE") * 100 # Negativity rate
)
overall_stats <- filtered_data %>%
summarise(
total_samples = n(),
total_positive = sum(biomarker_status == "POSITIVE"),
total_negative = sum(biomarker_status == "NEGATIVE"),
overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
)
combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)
# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])), # Transpose to get the correct format
names.arg = rate_by_bucket$ctDNA_bucket,
col = c("red", "blue"), # Colors: red for positive, blue for negative
main = '% ctDNA Positive and Negative Samples at the MRD Window',
xlab = 'Weeks from Surgery',
ylab = '% ctDNA Samples',
ylim = c(0, 100),
legend = c("% Positive", "% Negative"), # Adding a legend for clarification
args.legend = list(x = "topright")
)
par(new = TRUE)plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4) # Add the secondary y-axis on the rightmtext("Total Number of Samples", side = 4, line = 3) # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
filter(ctDNA.MRD >= 0 & ctDNA.MRD <= 10) #intervals for 0-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD,
breaks = c(0, 2, 4, 6, 8, 10),
right = FALSE,
labels = c("0-2","2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
group_by(ctDNA_bucket) %>%
summarise(
n_total = n(), # Total number of patients in the bucket
n_positive = sum(biomarker_status == "POSITIVE"), # Number of positive cases
n_negative = sum(biomarker_status == "NEGATIVE"), # Number of negative cases
percentage_positive = mean(biomarker_status == "POSITIVE") * 100, # Positivity rate
percentage_negative = mean(biomarker_status == "NEGATIVE") * 100 # Negativity rate
)
overall_stats <- filtered_data %>%
summarise(
total_samples = n(),
total_positive = sum(biomarker_status == "POSITIVE"),
total_negative = sum(biomarker_status == "NEGATIVE"),
overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
)
combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)
# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])), # Transpose to get the correct format
names.arg = rate_by_bucket$ctDNA_bucket,
col = c("red", "blue"), # Colors: red for positive, blue for negative
main = '% ctDNA Positive and Negative Samples at the MRD Window',
xlab = 'Weeks from Surgery',
ylab = '% ctDNA Samples',
ylim = c(0, 100),
legend = c("% Positive", "% Negative"), # Adding a legend for clarification
args.legend = list(x = "topright")
)
par(new = TRUE)plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4) # Add the secondary y-axis on the rightmtext("Total Number of Samples", side = 4, line = 3) # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)
#Median number of timepoints in the MRD Window
rm(list=ls())
setwd("~/Downloads")
filtered_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
filtered_data <- filtered_data[filtered_data$MRD.Window==TRUE,]
filtered_data <- filtered_data[!is.na(filtered_data$cfDNAconc), ]
filtered_data <- filtered_data %>%
filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks
# Calculate the median number of ctDNA tests per patient
median_ctDNA_tests <- filtered_data %>%
group_by(pts_id) %>%
summarise(num_tests = n()) %>%
summarise(median_tests = median(num_tests))
ctDNA_stats <- filtered_data %>%
group_by(pts_id) %>%
tally() %>%
summarise(
median_tests = median(n),
min_tests = min(n),
max_tests = max(n)
)
print(median_ctDNA_tests)print(ctDNA_stats)#Multivariate cox regression for DFS at the MRD Window & Age threshold as 50 years - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group2 + PrimSite + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
Variable = rownames(summary(cox_fit)$coefficients),
Original_P_Value = p_values,
FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)#Univariate cox regression for factors used in MVA - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Gender, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ Gender, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
GenderMale 0.3259 1.3853 0.2550 1.278 0.201
exp(coef) exp(-coef) lower .95 upper .95
GenderMale 1.385 0.7219 0.8404 2.284
Concordance= 0.535 (se = 0.032 )
Likelihood ratio test= 1.67 on 1 df, p=0.2
Wald test = 1.63 on 1 df, p=0.2
Score (logrank) test = 1.65 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.39 (0.84-2.28); p = 0.201"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50")) #univariate for Age
cox_fit <- coxph(surv_object ~ Age.Group2, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ Age.Group2, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
Age.Group2≥50 0.1934 1.2133 0.3102 0.623 0.533
exp(coef) exp(-coef) lower .95 upper .95
Age.Group2≥50 1.213 0.8242 0.6606 2.229
Concordance= 0.526 (se = 0.024 )
Likelihood ratio test= 0.4 on 1 df, p=0.5
Wald test = 0.39 on 1 df, p=0.5
Score (logrank) test = 0.39 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.21 (0.66-2.23); p = 0.533"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon")) #univariate for Tumor Location
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ PrimSite, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
PrimSiteLeft-sided colon 0.2250 1.2523 0.2482 0.907 0.365
exp(coef) exp(-coef) lower .95 upper .95
PrimSiteLeft-sided colon 1.252 0.7985 0.77 2.037
Concordance= 0.518 (se = 0.032 )
Likelihood ratio test= 0.82 on 1 df, p=0.4
Wald test = 0.82 on 1 df, p=0.4
Score (logrank) test = 0.83 on 1 df, p=0.4cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.25 (0.77-2.04); p = 0.365"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4")) #univariate for Overall T Stage
cox_fit <- coxph(surv_object ~ pT, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ pT, data = circ_data)
n= 386, number of events= 65
(4 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pTT3-T4 1.0960 2.9923 0.5163 2.123 0.0338 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pTT3-T4 2.992 0.3342 1.088 8.231
Concordance= 0.549 (se = 0.017 )
Likelihood ratio test= 6.26 on 1 df, p=0.01
Wald test = 4.51 on 1 df, p=0.03
Score (logrank) test = 4.98 on 1 df, p=0.03cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.99 (1.09-8.23); p = 0.034"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2")) #univariate for Overall N Stage
cox_fit <- coxph(surv_object ~ pN, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ pN, data = circ_data)
n= 388, number of events= 65
(2 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pNN1-N2 1.6966 5.4556 0.3592 4.723 2.32e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pNN1-N2 5.456 0.1833 2.698 11.03
Concordance= 0.667 (se = 0.023 )
Likelihood ratio test= 31.89 on 1 df, p=2e-08
Wald test = 22.31 on 1 df, p=2e-06
Score (logrank) test = 28.18 on 1 df, p=1e-07cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.46 (2.7-11.03); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High")) #univariate for MSI
cox_fit <- coxph(surv_object ~ MSI, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ MSI, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
MSIMSI-High 0.2018 1.2236 0.3312 0.609 0.542
exp(coef) exp(-coef) lower .95 upper .95
MSIMSI-High 1.224 0.8173 0.6393 2.342
Concordance= 0.531 (se = 0.028 )
Likelihood ratio test= 0.35 on 1 df, p=0.6
Wald test = 0.37 on 1 df, p=0.5
Score (logrank) test = 0.37 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.22 (0.64-2.34); p = 0.542"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E")) #univariate for BRAF
cox_fit <- coxph(surv_object ~ BRAF.V600E, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ BRAF.V600E, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
BRAF.V600EV600E 0.5464 1.7271 0.3199 1.708 0.0876 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
BRAF.V600EV600E 1.727 0.579 0.9226 3.233
Concordance= 0.552 (se = 0.028 )
Likelihood ratio test= 2.59 on 1 df, p=0.1
Wald test = 2.92 on 1 df, p=0.09
Score (logrank) test = 2.99 on 1 df, p=0.08cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.73 (0.92-3.23); p = 0.088"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut")) #univariate for RAS
cox_fit <- coxph(surv_object ~ RAS, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ RAS, data = circ_data)
n= 390, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
RASMut 0.1691 1.1843 0.2507 0.675 0.5
exp(coef) exp(-coef) lower .95 upper .95
RASMut 1.184 0.8444 0.7246 1.936
Concordance= 0.511 (se = 0.031 )
Likelihood ratio test= 0.45 on 1 df, p=0.5
Wald test = 0.46 on 1 df, p=0.5
Score (logrank) test = 0.46 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.18 (0.72-1.94); p = 0.5"#DFS by ctDNA at the MRD Window - Stage II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 141 5 NA NA NA
ctDNA.MRD=POSITIVE 10 3 NA 11.1 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 46.000 5.000 0.949 0.023 0.878 0.979
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 3.000 0.700 0.145 0.329 0.892 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 151, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.1194 8.3262 0.7317 2.897 0.00377 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 8.326 0.1201 1.985 34.93
Concordance= 0.68 (se = 0.09 )
Likelihood ratio test= 6.3 on 1 df, p=0.01
Wald test = 8.39 on 1 df, p=0.004
Score (logrank) test = 12.02 on 1 df, p=5e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 8.33 (1.98-34.93); p = 0.004"#DFS by ctDNA at the MRD Window - Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 170 24 NA NA NA
ctDNA.MRD=POSITIVE 51 33 10 7.13 16.2event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 57.0000 20.0000 0.8582 0.0303 0.7863 0.9073
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 33.0000 0.2704 0.0714 0.1433 0.4146 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 221, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.0479 7.7515 0.2728 7.506 6.08e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 7.752 0.129 4.541 13.23
Concordance= 0.731 (se = 0.031 )
Likelihood ratio test= 53.81 on 1 df, p=2e-13
Wald test = 56.35 on 1 df, p=6e-14
Score (logrank) test = 77.35 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.75 (4.54-13.23); p = 0"#DFS by ctDNA at the MRD Window - Stage II & Risk Groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$Risk.Group!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.Risk = case_when(
ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.II.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.Risk, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.Risk, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.Risk=1 30 1 NA NA NA
ctDNA.Stage.II.Risk=2 2 0 NA NA NA
ctDNA.Stage.II.Risk=3 110 4 NA NA NA
ctDNA.Stage.II.Risk=4 8 3 NA 11.1 NAevent_summary <- circ_data %>%
group_by(ctDNA.Stage.II.Risk) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.Risk, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage II Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.Risk, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.Risk=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 11.0000 1.0000 0.9600 0.0392 0.7484 0.9943
ctDNA.Stage.II.Risk=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 1 0 1 0 NA NA
ctDNA.Stage.II.Risk=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 35.0000 4.0000 0.9465 0.0278 0.8549 0.9809
ctDNA.Stage.II.Risk=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 5.000 3.000 0.625 0.171 0.229 0.861 circ_data$ctDNA.Stage.II.Risk <- factor(circ_data$ctDNA.Stage.II.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxphf(surv_object ~ ctDNA.Stage.II.Risk, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Stage.II.Risk, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.Stage.II.RiskctDNA(+) & Low Risk 1.54746514 1.827452 4.6995424 0.03219659 88.15970 0.670679229 0.41281490
ctDNA.Stage.II.RiskctDNA(-) & High Risk -0.06180044 1.054866 0.9400705 0.17388788 9.38239 0.004245486 0.94804868
ctDNA.Stage.II.RiskctDNA(+) & High Risk 2.30998495 1.092244 10.0742730 1.65326183 104.09858 6.196660240 0.01279916
Likelihood ratio test=9.240477 on 3 df, p=0.02625872, n=150
Wald test = 9.971067 on 3 df, p = 0.01881368
Covariance-Matrix:
ctDNA.Stage.II.RiskctDNA(+) & Low Risk ctDNA.Stage.II.RiskctDNA(-) & High Risk ctDNA.Stage.II.RiskctDNA(+) & High Risk
ctDNA.Stage.II.RiskctDNA(+) & Low Risk 3.3395808 0.8333478 0.8321149
ctDNA.Stage.II.RiskctDNA(-) & High Risk 0.8333478 1.1127416 0.8335422
ctDNA.Stage.II.RiskctDNA(+) & High Risk 0.8321149 0.8335422 1.1929966#DFS by ctDNA at the MRD Window - Stage III & Risk Groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.III.Risk = case_when(
ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.III.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.III.Risk, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.III.Risk=1 103 10 NA NA NA
ctDNA.Stage.III.Risk=2 13 9 12.9 6.14 NA
ctDNA.Stage.III.Risk=3 67 14 NA NA NA
ctDNA.Stage.III.Risk=4 38 24 9.0 6.01 NAevent_summary <- circ_data %>%
group_by(ctDNA.Stage.III.Risk) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18, 24))Call: survfit(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.III.Risk=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18 57 8 0.909 0.0312 0.825 0.954
24 34 1 0.889 0.0367 0.791 0.942
ctDNA.Stage.III.Risk=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 2.0000 8.0000 0.3077 0.1417 0.0793 0.5780
ctDNA.Stage.III.Risk=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18 38 11 0.814 0.0512 0.688 0.893
24 23 0 0.814 0.0512 0.688 0.893
ctDNA.Stage.III.Risk=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18 8 24 0.304 0.0812 0.157 0.464
24 6 0 0.304 0.0812 0.157 0.464circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data)
n= 221, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.III.RiskctDNA(+) & Low Risk 2.4892 12.0518 0.4642 5.362 8.23e-08 ***
ctDNA.Stage.III.RiskctDNA(-) & High Risk 0.7524 2.1221 0.4141 1.817 0.0692 .
ctDNA.Stage.III.RiskctDNA(+) & High Risk 2.3908 10.9222 0.3791 6.307 2.85e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.III.RiskctDNA(+) & Low Risk 12.052 0.08297 4.8517 29.937
ctDNA.Stage.III.RiskctDNA(-) & High Risk 2.122 0.47123 0.9425 4.778
ctDNA.Stage.III.RiskctDNA(+) & High Risk 10.922 0.09156 5.1955 22.961
Concordance= 0.757 (se = 0.033 )
Likelihood ratio test= 57.24 on 3 df, p=2e-12
Wald test = 55.9 on 3 df, p=4e-12
Score (logrank) test = 79.25 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.III.Risk = case_when(
ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.III.Risk, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.III.Risk=1 103 10 NA NA NA
ctDNA.Stage.III.Risk=2 13 9 12.9 6.14 NA
ctDNA.Stage.III.Risk=3 67 14 NA NA NA
ctDNA.Stage.III.Risk=4 38 24 9.0 6.01 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18))Call: survfit(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.III.Risk=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 57.0000 8.0000 0.9095 0.0312 0.8248 0.9543
ctDNA.Stage.III.Risk=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 2.0000 8.0000 0.3077 0.1417 0.0793 0.5780
ctDNA.Stage.III.Risk=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 38.0000 11.0000 0.8141 0.0512 0.6878 0.8932
ctDNA.Stage.III.Risk=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.0000 8.0000 24.0000 0.3038 0.0812 0.1574 0.4641 circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.III.Risk, data = circ_data)
n= 221, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.III.Risk4 -0.09842 0.90627 0.39258 -0.251 0.802
ctDNA.Stage.III.Risk1 -2.48922 0.08297 0.46424 -5.362 8.23e-08 ***
ctDNA.Stage.III.Risk3 -1.73680 0.17608 0.43279 -4.013 5.99e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.III.Risk4 0.90627 1.103 0.41985 1.9562
ctDNA.Stage.III.Risk1 0.08297 12.052 0.03340 0.2061
ctDNA.Stage.III.Risk3 0.17608 5.679 0.07539 0.4113
Concordance= 0.757 (se = 0.033 )
Likelihood ratio test= 57.24 on 3 df, p=2e-12
Wald test = 55.9 on 3 df, p=4e-12
Score (logrank) test = 79.25 on 3 df, p=<2e-16#OS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 334 8 NA NA NA
ctDNA.MRD=POSITIVE 71 7 40.4 40.4 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
2.40e+01 1.52e+02 7.00e+00 9.75e-01 9.49e-03 9.48e-01 9.88e-01
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 30.0000 5.0000 0.9064 0.0408 0.7856 0.9607 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 405, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.551 4.717 0.519 2.988 0.0028 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 4.717 0.212 1.705 13.04
Concordance= 0.633 (se = 0.068 )
Likelihood ratio test= 7.93 on 1 df, p=0.005
Wald test = 8.93 on 1 df, p=0.003
Score (logrank) test = 10.85 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.72 (1.71-13.04); p = 0.003"#DFS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 540 20 NA NA NA
ctDNA.Surveillance=POSITIVE 83 54 16.2 13.7 28.4event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
2.40e+01 2.83e+02 1.60e+01 9.64e-01 9.06e-03 9.41e-01 9.78e-01
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 21.0000 44.0000 0.4087 0.0597 0.2916 0.5222 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 623, number of events= 74
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.2926 26.9135 0.2645 12.45 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 26.91 0.03716 16.03 45.19
Concordance= 0.817 (se = 0.026 )
Likelihood ratio test= 173.2 on 1 df, p=<2e-16
Wald test = 155 on 1 df, p=<2e-16
Score (logrank) test = 344 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 26.91 (16.03-45.19); p = 0"#DFS by ctDNA at the Surveillance Window - Stages II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 198 6 NA NA NA
ctDNA.Surveillance=POSITIVE 16 9 21.2 11.1 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 99.000 5.000 0.967 0.015 0.920 0.986
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 3.000 7.000 0.480 0.149 0.187 0.725 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 214, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.4093 30.2449 0.5373 6.345 2.23e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 30.24 0.03306 10.55 86.7
Concordance= 0.782 (se = 0.063 )
Likelihood ratio test= 34.66 on 1 df, p=4e-09
Wald test = 40.26 on 1 df, p=2e-10
Score (logrank) test = 94.31 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 30.24 (10.55-86.7); p = 0"#DFS by ctDNA at the Surveillance Window - Stages III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 306 13 NA NA NA
ctDNA.Surveillance=POSITIVE 64 45 15.8 13.1 28.4event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 163.0000 10.0000 0.9610 0.0123 0.9281 0.9790
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 18.0000 37.0000 0.3800 0.0646 0.2553 0.5037 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 370, number of events= 58
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.2208 25.0476 0.3173 10.15 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 25.05 0.03992 13.45 46.65
Concordance= 0.827 (se = 0.027 )
Likelihood ratio test= 129.4 on 1 df, p=<2e-16
Wald test = 103 on 1 df, p=<2e-16
Score (logrank) test = 222.1 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 25.05 (13.45-46.65); p = 0"#OS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 540 4 NA NA NA
ctDNA.Surveillance=POSITIVE 83 4 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
2.40e+01 3.32e+02 3.00e+00 9.94e-01 3.65e-03 9.80e-01 9.98e-01
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 41.0000 3.0000 0.9594 0.0231 0.8785 0.9868 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 623, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 1.9570 7.0779 0.7088 2.761 0.00576 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 7.078 0.1413 1.764 28.39
Concordance= 0.687 (se = 0.094 )
Likelihood ratio test= 6.66 on 1 df, p=0.01
Wald test = 7.62 on 1 df, p=0.006
Score (logrank) test = 10.36 on 1 df, p=0.001cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.08 (1.76-28.39); p = 0.006"#ctDNA sample positive in the Surveillance Window - 6mo intervals
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
circ_data$ctDNA_bucket <- cut(circ_data$ctDNA.Surv,
breaks = seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6),
right = FALSE,
labels = paste0(seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE) - 6, by = 6),
"-",
seq(6, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6)))
rate_by_bucket <- circ_data %>%
group_by(ctDNA_bucket) %>%
summarise(
n_total = n(), # Total number of patients in the bucket
n_positive = sum(biomarker_status == "POSITIVE"), # Number of positive cases
n_negative = sum(biomarker_status == "NEGATIVE"), # Number of negative cases
percentage_positive = mean(biomarker_status == "POSITIVE") * 100, # Positivity rate
percentage_negative = mean(biomarker_status == "NEGATIVE") * 100 # Negativity rate
)
overall_totals <- circ_data %>%
summarise(
ctDNA_bucket = "Total", # Label for the total row
n_total = n(), # Total number of patients across all buckets
n_positive = sum(biomarker_status == "POSITIVE"), # Total number of positive cases across all buckets
percentage_positive = mean(biomarker_status == "POSITIVE") * 100, # Overall positivity rate
n_negative = sum(biomarker_status == "NEGATIVE"), # Total number of negative cases across all buckets
percentage_negative = mean(biomarker_status == "NEGATIVE") * 100 # Overall negativity rate
)
positivity_rate_with_total <- bind_rows(rate_by_bucket, overall_totals)
print(positivity_rate_with_total)bar_midpoints <- barplot(
t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])), # Transpose to get correct format
names.arg = rate_by_bucket$ctDNA_bucket,
col = c("red", "blue"), # Red for positivity and blue for negativity
main = '% ctDNA Positive and Negative Samples by 6-month Interval',
xlab = 'Time from Start of Surveillance (Months)',
ylab = '% ctDNA Samples',
ylim = c(0, 100),
legend = c("% Positive", "% Negative"), # Adding a legend for clarification
args.legend = list(x = "topright")
)
par(new = TRUE)plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4) # Add the secondary y-axis on the rightmtext("Total Number of Samples", side = 4, line = 3) # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)
#Median number of timepoints in the Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
# Calculate the median number of ctDNA tests per patient
ctDNA_stats <- circ_data %>%
group_by(pts_id) %>%
tally() %>%
summarise(
median_tests = median(n),
min_tests = min(n),
max_tests = max(n)
)
# Print the result
print(ctDNA_stats)#Time between imaging at the Surveillance window by ctDNA status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)ctDNA_Surveillance
NEGATIVE POSITIVE Total
151 22 173 median_AverageDateDifference <- aggregate(AverageDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_AverageDateDifference)circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(AverageDateDifference~ctDNA_Surveillance, data=circ_data, main="Average Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Average Time Between Imaging (Days)", col="white",border="black")
m1<-wilcox.test(AverageDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)
Wilcoxon rank sum test with continuity correction
data: AverageDateDifference by ctDNA_Surveillance
W = 2327, p-value = 6.322e-06
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
106.25 269.00
sample estimates:
difference in location
181 rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)ctDNA_Surveillance
NEGATIVE POSITIVE Total
151 22 173 median_MedianDateDifference <- aggregate(MedianDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_MedianDateDifference)circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(MedianDateDifference~ctDNA_Surveillance, data=circ_data, main="Median Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Median Time Between Imaging (Days)", col="white",border="black")
m2<-wilcox.test(MedianDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)
Wilcoxon rank sum test with continuity correction
data: MedianDateDifference by ctDNA_Surveillance
W = 2336.5, p-value = 5.008e-06
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
90.00009 269.00006
sample estimates:
difference in location
158.7506 #Time-dependent analysis in surveillance window - all stages
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)Warning: Stop time must be > start time, NA createdsummary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 3456, number of events= 95
(1211 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.6369 37.9742 0.2235 16.27 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 37.97 0.02633 24.51 58.84
Concordance= 0.816 (se = 0.024 )
Likelihood ratio test= 256.4 on 1 df, p=<2e-16
Wald test = 264.9 on 1 df, p=<2e-16
Score (logrank) test = 679.7 on 1 df, p=<2e-16#Time-dependent analysis in surveillance window - all stages ACT-treated
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)Warning: Stop time must be > start time, NA createdsummary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 2178, number of events= 69
(1014 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.5174 33.6971 0.2601 13.52 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 33.7 0.02968 20.24 56.11
Concordance= 0.807 (se = 0.029 )
Likelihood ratio test= 175.9 on 1 df, p=<2e-16
Wald test = 182.8 on 1 df, p=<2e-16
Score (logrank) test = 443.9 on 1 df, p=<2e-16#Time-dependent analysis in surveillance window - all stages Non-ACT-treated
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="FALSE",]
dt_final <- dt_final[dt_final$tstart!="",]
datatable(dt_final, filter = "top")
# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
# data = dt_final)
# summary(fit)
fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)Warning: Stop time must be > start time, NA createdsummary(fit)Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status,
data = dt_final)
n= 1278, number of events= 26
(197 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 3.8078 45.0513 0.4362 8.73 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 45.05 0.0222 19.16 105.9
Concordance= 0.835 (se = 0.043 )
Likelihood ratio test= 76.31 on 1 df, p=<2e-16
Wald test = 76.21 on 1 df, p=<2e-16
Score (logrank) test = 214.3 on 1 df, p=<2e-16#DFS by ctDNA subgroups at the Surveillance window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~Surveillance.Groups, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
Surveillance.Groups, data = circ_data)
n events median 0.95LCL 0.95UCL
Surveillance.Groups=All Negative 540 20 NA NA NA
Surveillance.Groups=All Positive 35 30 8.05 6.9 13.6
Surveillance.Groups=Negative-Positive 48 24 31.04 21.3 NAevent_summary <- circ_data %>%
group_by(Surveillance.Groups) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("All Negative","Negative-Positive","All Positive"))
KM_curve <- survfit(surv_object ~ Surveillance.Groups, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","purple", "red"), title="DFS - ctDNA Subgroups Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All Negative","Negative-Positive","All Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ Surveillance.Groups, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
Surveillance.Groups=All Negative
time n.risk n.event survival std.err lower 95% CI upper 95% CI
2.40e+01 2.83e+02 1.60e+01 9.64e-01 9.06e-03 9.41e-01 9.78e-01
Surveillance.Groups=Negative-Positive
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 17.0000 17.0000 0.5803 0.0801 0.4086 0.7182
Surveillance.Groups=All Positive
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 4.0000 27.0000 0.1657 0.0702 0.0576 0.3222 cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ Surveillance.Groups, data = circ_data)
n= 623, number of events= 74
coef exp(coef) se(coef) z Pr(>|z|)
Surveillance.GroupsNegative-Positive 2.7806 16.1288 0.3041 9.144 <2e-16 ***
Surveillance.GroupsAll Positive 4.2247 68.3543 0.3033 13.929 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
Surveillance.GroupsNegative-Positive 16.13 0.06200 8.887 29.27
Surveillance.GroupsAll Positive 68.35 0.01463 37.721 123.86
Concordance= 0.833 (se = 0.027 )
Likelihood ratio test= 198.8 on 2 df, p=<2e-16
Wald test = 196.3 on 2 df, p=<2e-16
Score (logrank) test = 536.6 on 2 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("Negative-Positive","All Positive", "All Negative"))
cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ Surveillance.Groups, data = circ_data)
n= 623, number of events= 74
coef exp(coef) se(coef) z Pr(>|z|)
Surveillance.GroupsAll Positive 1.4441 4.2380 0.2826 5.111 3.2e-07 ***
Surveillance.GroupsAll Negative -2.7806 0.0620 0.3041 -9.144 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
Surveillance.GroupsAll Positive 4.238 0.236 2.43591 7.3734
Surveillance.GroupsAll Negative 0.062 16.129 0.03416 0.1125
Concordance= 0.833 (se = 0.027 )
Likelihood ratio test= 198.8 on 2 df, p=<2e-16
Wald test = 196.3 on 2 df, p=<2e-16
Score (logrank) test = 536.6 on 2 df, p=<2e-16#Demographics table for ctDNA positive at the Surveillance window with no radiological recurrence
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance=="POSITIVE",]
circ_data <- circ_data[circ_data$DFS.Event==FALSE,]
circ_data_subset <- circ_data %>%
select(
Age,
Gender,
PrimSite,
pT,
pN,
Stage,
Grade,
NAC,
ACT,
MSI,
BRAF.V600E,
RAS,
OS.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
pN = factor(pN, levels = c("N0", "N1-N2")),
Stage = factor(Stage, levels = c("I","II", "III")),
Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
MSI = factor(MSI, levels = c("MSS", "MSI-High")),
BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 291 |
|---|---|
| Age | 57 (28 - 86) |
| Gender | |
| Male | 12 (41%) |
| Female | 17 (59%) |
| PrimSite | |
| Right-sided colon | 12 (41%) |
| Left-sided colon | 17 (59%) |
| pT | |
| T0 | 0 (0%) |
| T1-T2 | 4 (14%) |
| T3-T4 | 25 (86%) |
| pN | |
| N0 | 9 (31%) |
| N1-N2 | 20 (69%) |
| Stage | |
| I | 3 (10%) |
| II | 7 (24%) |
| III | 19 (66%) |
| Grade | |
| G1 | 4 (14%) |
| G2 | 19 (66%) |
| G3 | 6 (21%) |
| GX | 0 (0%) |
| NAC | |
| Neoadjuvant Chemotherapy | 0 (0%) |
| Upfront Surgery | 29 (100%) |
| ACT | |
| Adjuvant Chemotherapy | 19 (66%) |
| Observation | 10 (34%) |
| MSI | |
| MSS | 26 (90%) |
| MSI-High | 3 (10%) |
| BRAF.V600E | |
| BRAF WT | 26 (90%) |
| BRAF V600E | 3 (10%) |
| RAS | |
| RAS WT | 17 (59%) |
| RAS Mut | 12 (41%) |
| OS.months | 22 (7 - 62) |
| 1 Median (Range); n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE,
strip_md_bold = TRUE)
fit1Characteristic | N = 291 |
|---|---|
Age | 57 (28 - 86) |
Gender | |
Male | 12 (41%) |
Female | 17 (59%) |
PrimSite | |
Right-sided colon | 12 (41%) |
Left-sided colon | 17 (59%) |
pT | |
T0 | 0 (0%) |
T1-T2 | 4 (14%) |
T3-T4 | 25 (86%) |
pN | |
N0 | 9 (31%) |
N1-N2 | 20 (69%) |
Stage | |
I | 3 (10%) |
II | 7 (24%) |
III | 19 (66%) |
Grade | |
G1 | 4 (14%) |
G2 | 19 (66%) |
G3 | 6 (21%) |
GX | 0 (0%) |
NAC | |
Neoadjuvant Chemotherapy | 0 (0%) |
Upfront Surgery | 29 (100%) |
ACT | |
Adjuvant Chemotherapy | 19 (66%) |
Observation | 10 (34%) |
MSI | |
MSS | 26 (90%) |
MSI-High | 3 (10%) |
BRAF.V600E | |
BRAF WT | 26 (90%) |
BRAF V600E | 3 (10%) |
RAS | |
RAS WT | 17 (59%) |
RAS Mut | 12 (41%) |
OS.months | 22 (7 - 62) |
1Median (Range); n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#DFS by ctDNA at 12 months post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.12months!="",]
circ_data$DFS.months=circ_data$DFS.months-12
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.12months, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.12months, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.12months=NEGATIVE 136 6 NA NA NA
ctDNA.12months=POSITIVE 14 7 6.69 3.74 NAevent_summary <- circ_data %>%
group_by(ctDNA.12months) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.12months, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA at 12 months | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.12months, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.12months=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 31.0000 6.0000 0.9365 0.0263 0.8593 0.9720
ctDNA.12months=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 1.000 7.000 0.373 0.156 0.105 0.650 circ_data$ctDNA.12months <- factor(circ_data$ctDNA.12months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.12months, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.12months, data = circ_data)
n= 150, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.12monthsPOSITIVE 2.8501 17.2889 0.5708 4.993 5.94e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.12monthsPOSITIVE 17.29 0.05784 5.648 52.92
Concordance= 0.748 (se = 0.068 )
Likelihood ratio test= 20.99 on 1 df, p=5e-06
Wald test = 24.93 on 1 df, p=6e-07
Score (logrank) test = 45.91 on 1 df, p=1e-11cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 17.29 (5.65-52.92); p = 0"#DFS by ctDNA 4-weeks post-adjuvant chemotherapy
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postACT!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.postACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.postACT=NEGATIVE 112 17 NA NA NA
ctDNA.postACT=POSITIVE 6 4 7.54 3.45 NAevent_summary <- circ_data %>%
group_by(ctDNA.postACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 4 weeks post-ACT | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.postACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.postACT=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 42.000 14.000 0.845 0.039 0.749 0.906
ctDNA.postACT=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CIcirc_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postACT, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.postACT, data = circ_data)
n= 118, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.postACTPOSITIVE 2.4960 12.1334 0.5794 4.308 1.65e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.postACTPOSITIVE 12.13 0.08242 3.897 37.77
Concordance= 0.612 (se = 0.051 )
Likelihood ratio test= 11.57 on 1 df, p=7e-04
Wald test = 18.56 on 1 df, p=2e-05
Score (logrank) test = 30.09 on 1 df, p=4e-08cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 12.13 (3.9-37.77); p = 0"#DFS by ACT treatment in MRD negative - High Risk Stage II or Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 93 5 NA NA NA
ACT=TRUE 187 23 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 30.0000 5.0000 0.9186 0.0372 0.8054 0.9673
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 68.000 19.000 0.878 0.027 0.813 0.921 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 280, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE -0.7813 0.4578 0.4935 -1.583 0.113
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 0.4578 2.184 0.174 1.204
Concordance= 0.571 (se = 0.04 )
Likelihood ratio test= 2.93 on 1 df, p=0.09
Wald test = 2.51 on 1 df, p=0.1
Score (logrank) test = 2.64 on 1 df, p=0.1cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.46 (0.17-1.2); p = 0.113"#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + MSI + Stage, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Age.Group + Gender + MSI +
Stage, data = circ_data)
n= 280, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE -0.1808 0.8346 0.6123 -0.295 0.7678
Age.Group≥70 0.3185 1.3750 0.4494 0.709 0.4785
GenderMale 0.5723 1.7724 0.4009 1.428 0.1534
MSIMSI-High 0.2271 1.2550 0.5780 0.393 0.6944
StageIII 1.3172 3.7330 0.6395 2.060 0.0394 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 0.8346 1.1982 0.2513 2.772
Age.Group≥70 1.3750 0.7273 0.5699 3.318
GenderMale 1.7724 0.5642 0.8078 3.889
MSIMSI-High 1.2550 0.7968 0.4042 3.896
StageIII 3.7330 0.2679 1.0658 13.075
Concordance= 0.668 (se = 0.055 )
Likelihood ratio test= 10.58 on 5 df, p=0.06
Wald test = 8.62 on 5 df, p=0.1
Score (logrank) test = 9.59 on 5 df, p=0.09# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
Variable = rownames(summary(cox_fit)$coefficients),
Original_P_Value = p_values,
FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)#DFS by ACT treatment in MRD positive - High Risk Stage II or Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 10 9 2.41 1.22 NA
ACT=TRUE 51 29 13.38 10.19 NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 9.00000 0.10000 0.09487 0.00572 0.35813
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 11.0000 29.0000 0.3632 0.0744 0.2218 0.5060 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 61, number of events= 38
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.3968 4.0422 0.3909 3.573 0.000353 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 4.042 0.2474 1.879 8.697
Concordance= 0.613 (se = 0.036 )
Likelihood ratio test= 9.94 on 1 df, p=0.002
Wald test = 12.77 on 1 df, p=4e-04
Score (logrank) test = 14.86 on 1 df, p=1e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.04 (1.88-8.7); p = 0"#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + Stage, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Age.Group + Gender + Stage,
data = circ_data)
n= 61, number of events= 38
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.9310 6.8967 0.4580 4.216 2.48e-05 ***
Age.Group≥70 0.5599 1.7505 0.3603 1.554 0.1202
GenderMale 0.2086 1.2320 0.3471 0.601 0.5478
StageIII 1.3374 3.8093 0.5942 2.251 0.0244 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 6.897 0.1450 2.8106 16.923
Age.Group≥70 1.750 0.5713 0.8640 3.547
GenderMale 1.232 0.8117 0.6239 2.432
StageIII 3.809 0.2625 1.1887 12.208
Concordance= 0.669 (se = 0.044 )
Likelihood ratio test= 18.74 on 4 df, p=9e-04
Wald test = 20.61 on 4 df, p=4e-04
Score (logrank) test = 22.94 on 4 df, p=1e-04# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
Variable = rownames(summary(cox_fit)$coefficients),
Original_P_Value = p_values,
FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)#DFS by ctDNA at the MRD Window & ACT - MSS Stable all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 169 19 NA NA NA
ctDNA.ACT=2 48 27 13.07 9.69 NA
ctDNA.ACT=3 106 6 NA NA NA
ctDNA.ACT=4 3 2 7.42 5.29 NAevent_summary <- circ_data %>%
group_by(ctDNA.ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 61.000 15.000 0.892 0.027 0.825 0.934
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 9.0000 27.0000 0.3640 0.0773 0.2174 0.5120
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 37.000 6.000 0.921 0.032 0.829 0.965
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 2.00000 0.33333 0.27217 0.00896 0.77415 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.ACT, data = circ_data)
n= 326, number of events= 54
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.ACTctDNA(+) & ACT 2.0237 7.5661 0.3021 6.700 2.09e-11 ***
ctDNA.ACTctDNA(-) & No ACT -0.6820 0.5056 0.4683 -1.456 0.14536
ctDNA.ACTctDNA(+) & No ACT 2.1276 8.3950 0.7463 2.851 0.00436 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.ACTctDNA(+) & ACT 7.5661 0.1322 4.1856 13.677
ctDNA.ACTctDNA(-) & No ACT 0.5056 1.9778 0.2019 1.266
ctDNA.ACTctDNA(+) & No ACT 8.3950 0.1191 1.9443 36.247
Concordance= 0.759 (se = 0.034 )
Likelihood ratio test= 61 on 3 df, p=4e-13
Wald test = 65.55 on 3 df, p=4e-14
Score (logrank) test = 98.87 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 169 19 NA NA NA
ctDNA.ACT=2 48 27 13.07 9.69 NA
ctDNA.ACT=3 106 6 NA NA NA
ctDNA.ACT=4 3 2 7.42 5.29 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 61.000 15.000 0.892 0.027 0.825 0.934
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 9.0000 27.0000 0.3640 0.0773 0.2174 0.5120
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 37.000 6.000 0.921 0.032 0.829 0.965
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 2.00000 0.33333 0.27217 0.00896 0.77415 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & No ACT","ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.ACT, data = circ_data)
n= 326, number of events= 54
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.ACTctDNA(-) & ACT 0.6820 1.9778 0.4683 1.456 0.145361
ctDNA.ACTctDNA(+) & ACT 2.7056 14.9638 0.4533 5.969 2.39e-09 ***
ctDNA.ACTctDNA(+) & No ACT 2.8096 16.6032 0.8192 3.430 0.000605 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.ACTctDNA(-) & ACT 1.978 0.50562 0.7898 4.953
ctDNA.ACTctDNA(+) & ACT 14.964 0.06683 6.1544 36.383
ctDNA.ACTctDNA(+) & No ACT 16.603 0.06023 3.3331 82.704
Concordance= 0.759 (se = 0.034 )
Likelihood ratio test= 61 on 3 df, p=4e-13
Wald test = 65.55 on 3 df, p=4e-14
Score (logrank) test = 98.87 on 3 df, p=<2e-16#DFS by ctDNA at the MRD Window & ACT - MSI High all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 19 4 NA NA NA
ctDNA.ACT=2 5 2 NA 3.09 NA
ctDNA.ACT=3 34 0 NA NA NA
ctDNA.ACT=4 6 5 1.91 1.41 NAevent_summary <- circ_data %>%
group_by(ctDNA.ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6, 24))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 15 3 0.839 0.0854 0.579 0.945
24 3 1 0.774 0.1003 0.502 0.910
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 3 2 0.6 0.219 0.126 0.882
24 2 0 0.6 0.219 0.126 0.882
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 31 0 1 0 NA NA
24 9 0 1 0 NA NA
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.00000 1.00000 5.00000 0.16667 0.15215 0.00772 0.51680 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500, maxstep = 1) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.ACT, data = circ_data, maxit = 500,
maxstep = 1)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.ACTctDNA(+) & ACT 2.813027 1.622557 16.66027 1.778749 2207.805 6.609234 1.014513e-02
ctDNA.ACTctDNA(-) & No ACT 3.712830 1.686348 40.96957 3.329543 5652.756 8.497597 3.556157e-03
ctDNA.ACTctDNA(+) & No ACT 4.942760 1.627985 140.15657 14.930527 18714.894 24.151928 8.902699e-07
Likelihood ratio test=26.64366 on 3 df, p=6.992091e-06, n=64
Wald test = 14.30441 on 3 df, p = 0.002518759
Covariance-Matrix:
ctDNA.ACTctDNA(+) & ACT ctDNA.ACTctDNA(-) & No ACT ctDNA.ACTctDNA(+) & No ACT
ctDNA.ACTctDNA(+) & ACT 2.632691 2.370004 2.371709
ctDNA.ACTctDNA(-) & No ACT 2.370004 2.843769 2.373722
ctDNA.ACTctDNA(+) & No ACT 2.371709 2.373722 2.650336rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.ACT = case_when(
ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.ACT=1 19 4 NA NA NA
ctDNA.ACT=2 5 2 NA 3.09 NA
ctDNA.ACT=3 34 0 NA NA NA
ctDNA.ACT=4 6 5 1.91 1.41 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6))Call: survfit(formula = surv_object ~ ctDNA.ACT, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.ACT=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.0000 15.0000 3.0000 0.8388 0.0854 0.5788 0.9451
ctDNA.ACT=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.000 3.000 2.000 0.600 0.219 0.126 0.882
ctDNA.ACT=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 31 0 1 0 NA NA
ctDNA.ACT=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.00000 1.00000 5.00000 0.16667 0.15215 0.00772 0.51680 circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("2","4","1","3"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.ACT, data = circ_data, maxit = 500)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.ACT4 1.2299304 0.8640952 3.42099155 0.771944824 20.3070261 2.600353 0.106839899
ctDNA.ACT1 -0.8998028 0.8581680 0.40664983 0.090118999 2.3336235 1.163885 0.280661276
ctDNA.ACT3 -3.7128298 1.6863479 0.02440835 0.000176905 0.3003415 8.497597 0.003556157
Likelihood ratio test=26.64366 on 3 df, p=6.992091e-06, n=64
Wald test = 14.30441 on 3 df, p = 0.002518759
Covariance-Matrix:
ctDNA.ACT4 ctDNA.ACT1 ctDNA.ACT3
ctDNA.ACT4 0.7466605 0.4717519 0.4700468
ctDNA.ACT1 0.4717519 0.7364524 0.4737650
ctDNA.ACT3 0.4700468 0.4737650 2.8437691#DFS by ctDNA Dynamics from MRD to Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 263 10 NA NA NA
ctDNA.Dynamics=2 16 1 NA NA NA
ctDNA.Dynamics=3 25 11 28.4 15.77 NA
ctDNA.Dynamics=4 20 15 10.6 8.05 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 93.000 8.000 0.961 0.014 0.922 0.981
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 8.0000 1.0000 0.9000 0.0949 0.4730 0.9853
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 9.000 0.519 0.125 0.260 0.727
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.2400 0.0980 0.0821 0.4428 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 324, number of events= 37
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics2 0.5229 1.6869 1.0493 0.498 0.618
ctDNA.Dynamics3 2.6136 13.6480 0.4383 5.963 2.48e-09 ***
ctDNA.Dynamics4 3.5381 34.4018 0.4141 8.545 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics2 1.687 0.59281 0.2157 13.19
ctDNA.Dynamics3 13.648 0.07327 5.7804 32.22
ctDNA.Dynamics4 34.402 0.02907 15.2803 77.45
Concordance= 0.822 (se = 0.04 )
Likelihood ratio test= 79.02 on 3 df, p=<2e-16
Wald test = 77.71 on 3 df, p=<2e-16
Score (logrank) test = 168.8 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 263 10 NA NA NA
ctDNA.Dynamics=2 16 1 NA NA NA
ctDNA.Dynamics=3 25 11 28.4 15.77 NA
ctDNA.Dynamics=4 20 15 10.6 8.05 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 93.000 8.000 0.961 0.014 0.922 0.981
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 8.0000 1.0000 0.9000 0.0949 0.4730 0.9853
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 9.000 0.519 0.125 0.260 0.727
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.2400 0.0980 0.0821 0.4428 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 324, number of events= 37
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics4 0.92452 2.52065 0.39952 2.314 0.0207 *
ctDNA.Dynamics1 -2.61359 0.07327 0.43834 -5.963 2.48e-09 ***
ctDNA.Dynamics2 -2.09071 0.12360 1.04642 -1.998 0.0457 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics4 2.52065 0.3967 1.15197 5.515
ctDNA.Dynamics1 0.07327 13.6480 0.03103 0.173
ctDNA.Dynamics2 0.12360 8.0907 0.01590 0.961
Concordance= 0.822 (se = 0.04 )
Likelihood ratio test= 79.02 on 3 df, p=<2e-16
Wald test = 77.71 on 3 df, p=<2e-16
Score (logrank) test = 168.8 on 3 df, p=<2e-16#DFS by ctDNA Dynamics from MRD to Surveillance Window - High Risk Stage II or Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 224 10 NA NA NA
ctDNA.Dynamics=2 14 1 NA NA NA
ctDNA.Dynamics=3 22 10 28.4 16.03 NA
ctDNA.Dynamics=4 19 15 10.1 8.05 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 77.0000 8.0000 0.9539 0.0166 0.9073 0.9774
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 7.000 1.000 0.889 0.105 0.433 0.984
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 8.000 0.542 0.128 0.272 0.750
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.1974 0.0948 0.0551 0.4032 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 279, number of events= 36
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics2 0.4503 1.5688 1.0494 0.429 0.668
ctDNA.Dynamics3 2.4212 11.2599 0.4483 5.401 6.64e-08 ***
ctDNA.Dynamics4 3.4366 31.0803 0.4147 8.286 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics2 1.569 0.63743 0.2006 12.27
ctDNA.Dynamics3 11.260 0.08881 4.6765 27.11
ctDNA.Dynamics4 31.080 0.03217 13.7872 70.06
Concordance= 0.816 (se = 0.04 )
Likelihood ratio test= 72.14 on 3 df, p=1e-15
Wald test = 72.35 on 3 df, p=1e-15
Score (logrank) test = 150.9 on 3 df, p=<2e-16rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
)) %>%
filter(!is.na(ctDNA.Dynamics))
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 224 10 NA NA NA
ctDNA.Dynamics=2 14 1 NA NA NA
ctDNA.Dynamics=3 22 10 28.4 16.03 NA
ctDNA.Dynamics=4 19 15 10.1 8.05 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 77.0000 8.0000 0.9539 0.0166 0.9073 0.9774
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 7.000 1.000 0.889 0.105 0.433 0.984
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 8.000 0.542 0.128 0.272 0.750
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 15.0000 0.1974 0.0948 0.0551 0.4032 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 279, number of events= 36
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics4 1.01533 2.76027 0.41237 2.462 0.0138 *
ctDNA.Dynamics1 -2.42124 0.08881 0.44832 -5.401 6.64e-08 ***
ctDNA.Dynamics2 -1.97094 0.13933 1.05059 -1.876 0.0607 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics4 2.76027 0.3623 1.23012 6.1938
ctDNA.Dynamics1 0.08881 11.2599 0.03689 0.2138
ctDNA.Dynamics2 0.13933 7.1774 0.01777 1.0922
Concordance= 0.816 (se = 0.04 )
Likelihood ratio test= 72.14 on 3 df, p=1e-15
Wald test = 72.35 on 3 df, p=1e-15
Score (logrank) test = 150.9 on 3 df, p=<2e-16#Median cfDNA concentration across different windows post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
median_cfDNAconc <- circ_data %>%
group_by(Timepoint) %>%
dplyr::summarize(
median_cfDNAconc = median(cfDNAconc, na.rm = TRUE),
n_observations = n(),
.groups = "drop"
)
print(median_cfDNAconc)boxplot(cfDNAconc~Timepoint, data=circ_data, main="median cfDNA Concentration", xlab="Intervals from Surgery", ylab="cfDNA Concentration", col="white",border="black", ylim = c(0, 40))
#Pairwise Wilcoxon-test p values
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
# Initialize a matrix to store p-values for manual Wilcoxon tests
timepoints <- levels(circ_data$Timepoint)
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints
# Perform pairwise Wilcoxon tests manually and store p-values
for (i in 1:length(timepoints)) {
for (j in i:length(timepoints)) {
if (i != j) {
# Subset data for the two Timepoints
data1 <- circ_data %>% filter(Timepoint == timepoints[i]) %>% pull(cfDNAconc)
data2 <- circ_data %>% filter(Timepoint == timepoints[j]) %>% pull(cfDNAconc)
# Perform Wilcoxon test and store the p-value
test_result <- wilcox.test(data1, data2, exact = FALSE)
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Symmetric matrix
} else {
p_value_matrix[i, j] <- 1 # Set diagonal to 1 for clarity
}
}
}
# Replace NA values with 1 in the p-value matrix for completeness
p_value_matrix[is.na(p_value_matrix)] <- 1.00
# Melt the p-value matrix for plotting
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")
# Add asterisks based on p-value thresholds
p_value_data <- p_value_data %>%
mutate(
asterisk = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
geom_tile(color = "white") + # Background tiles for grid
geom_text(aes(label = asterisk), color = "black", vjust = -0.5) + # Add asterisks
scale_fill_gradient(low = "lightgreen", high = "red") + # Gradient for p-values
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Pairwise Wilcoxon-Test P-Values",
fill = "P-Value")#ctDNA positivity across different windows post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
rate_by_timepoint <- circ_data %>%
group_by(Timepoint) %>%
summarise(
n_total = n(), # Total number of patients in each Timepoint
n_positive = sum(biomarker_status == "POSITIVE"), # Number of positive cases
n_negative = sum(biomarker_status == "NEGATIVE"), # Number of negative cases
percentage_positive = mean(biomarker_status == "POSITIVE") * 100, # Positivity rate
percentage_negative = mean(biomarker_status == "NEGATIVE") * 100 # Negativity rate
)
print(rate_by_timepoint)
# Create the stacked bar plot for positivity and negativity rates by Timepoint
bar_midpoints <- barplot(
t(as.matrix(rate_by_timepoint[, c("percentage_positive", "percentage_negative")])), # Transpose to get the correct format
names.arg = rate_by_timepoint$Timepoint,
col = c("red", "blue"), # Colors: red for positive, blue for negative
main = '% ctDNA Positive and Negative Samples by Timepoint',
xlab = 'Timepoint',
ylab = '% ctDNA Samples',
ylim = c(0, 100),
legend = c("% Positive", "% Negative"), # Adding a legend for clarification
args.legend = list(x = "topright")
)
par(new = TRUE)plot(bar_midpoints, rate_by_timepoint$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4) # Add the secondary y-axis on the rightmtext("Total Number of Samples", side = 4, line = 3) # Label for the secondary y-axis
text(bar_midpoints, rate_by_timepoint$n_total + 3, labels = rate_by_timepoint$n_total, col = "black", cex = 0.8)
#Perform fisher's exact test matrix and heatmap
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
timepoints <- levels(circ_data$Timepoint) # Use ordered levels
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints
for (i in 1:length(timepoints)) {
for (j in i:length(timepoints)) {
if (i != j) {
# Subset data for the two Timepoints
subset1 <- circ_data %>% filter(Timepoint == timepoints[i])
subset2 <- circ_data %>% filter(Timepoint == timepoints[j])
# Create contingency table
contingency_table <- matrix(c(
sum(subset1$biomarker_status == "POSITIVE"), sum(subset1$biomarker_status == "NEGATIVE"),
sum(subset2$biomarker_status == "POSITIVE"), sum(subset2$biomarker_status == "NEGATIVE")
), nrow = 2, byrow = TRUE)
# Perform Fisher's exact test
test_result <- fisher.test(contingency_table)
# Store p-value in the matrix
p_value_matrix[i, j] <- test_result$p.value
p_value_matrix[j, i] <- test_result$p.value # Symmetric matrix
} else {
p_value_matrix[i, j] <- 1 # Set diagonal to 1 for clarity
}
}
}
print(p_value_matrix) 0-2 weeks 2-4 weeks 4-6 weeks 6-8 weeks 8-10 weeks On-treatment Surveillance
0-2 weeks 1.0000000 1.000000000 6.919353e-01 2.857000e-01 0.6738454083 1.000000e+00 4.643687e-01
2-4 weeks 1.0000000 1.000000000 1.588616e-01 1.035825e-02 0.2594894746 1.000000e+00 2.706764e-03
4-6 weeks 0.6919353 0.158861559 1.000000e+00 1.913544e-01 1.0000000000 5.680924e-02 8.035880e-09
6-8 weeks 0.2857000 0.010358245 1.913544e-01 1.000000e+00 0.4193440867 2.126873e-03 1.724838e-09
8-10 weeks 0.6738454 0.259489475 1.000000e+00 4.193441e-01 1.0000000000 1.498403e-01 3.034774e-04
On-treatment 1.0000000 1.000000000 5.680924e-02 2.126873e-03 0.1498402610 1.000000e+00 6.108761e-08
Surveillance 0.4643687 0.002706764 8.035880e-09 1.724838e-09 0.0003034774 6.108761e-08 1.000000e+00p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")
p_value_data <- p_value_data %>%
mutate(
asterisk = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
)
# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
geom_tile(color = "white") + # Background tiles for grid
geom_text(aes(label = asterisk), color = "black", vjust = -0.5) + # Add asterisks
scale_fill_gradient(low = "lightgreen", high = "red") + # Gradient for p-values
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Pairwise Fisher's Exact Test P-Values",
x = "Timepoint 1",
y = "Timepoint 2",
fill = "P-Value")
#Logistic regression for ctDNA positivity
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
# Calculate the 20th, 40th, 60th, 80th, and 100th percentiles for cfDNAconc
thresholds <- quantile(circ_data$cfDNAconc, probs = seq(0, 1, 0.2), na.rm = TRUE)
print(thresholds) 0% 20% 40% 60% 80% 100%
1.125000 3.566842 4.764706 6.244382 9.357955 265.185000 circ_data <- circ_data %>%
mutate(cfDNAlevels = cut(cfDNAconc,
breaks = thresholds,
include.lowest = TRUE,
labels = c("Q1", "Q2", "Q3", "Q4", "Q5")))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
circ_data$biomarker_status <- factor(circ_data$biomarker_status, levels = c("NEGATIVE", "POSITIVE"))
circ_data$cfDNAlevels <- factor(circ_data$cfDNAlevels, levels = c("Q3", "Q1", "Q2", "Q4", "Q5"), labels = c("4.76-6.24 ng/mL (q3)", "<3.57 ng/mL (q1)", "3.57-4.76 ng/mL (q2)", "6.24-9.36 ng/mL (q4)", ">9.36 ng/mL (q5)"))
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("4-6 weeks", "0-2 weeks", "2-4 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("I", "II", "III"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
logistic_model <- glm(biomarker_status ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS,
data = circ_data,
family = binomial)
# Extract odds ratios and confidence intervals
results <- tidy(logistic_model, conf.int = TRUE, exponentiate = TRUE) %>%
mutate(term = factor(term, levels = term)) # Preserve term ordering
ggplot(results, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
geom_pointrange() +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray") + # Reference line at OR = 1
coord_flip() +
labs(title = "Odds Ratios for Biomarker Status (Forest Plot)",
x = "Predictor",
y = "Odds Ratio (95% CI)") +
theme_minimal()
cox_model <- coxph(Surv(rep(1, nrow(circ_data)), biomarker_status == "POSITIVE") ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS,
data = circ_data)
ggforest(cox_model, data = circ_data, main = "Odds Ratios for Biomarker Status", cpositions = c(0.02, 0.22, 0.4))
# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_model)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
Variable = rownames(summary(cox_model)$coefficients),
Original_P_Value = p_values,
FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)---
title: "Cohen et al_CLIA CRC_Clinical analysis 11022024"
output: html_notebook
---

library(swimplot)
library(pheatmap)
library(reshape2)
library(coxphf)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival)
library(broom)
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(ComplexHeatmap)
library(tidyverse)
library(readxl)
library(survival)
library(janitor)
library(openxlsx)
library(writexl)
library(rms)
library(DT)

#ctDNA Detection rate by Stage and Window
```{r}
#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Anytime post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```

#ctDNA MRD Detection rate Stage I/II vs III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("I", "II"), "I/II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
print(chi_square_test)
```

#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    pT,
    pN,
    Stage,
    Grade,
    NAC,
    ACT,
    MSI,
    BRAF.V600E,
    RAS,
    DFS.Event,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
    pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    Stage = factor(Stage, levels = c("I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
    DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```


#Heatmap with Clinical & Genomics Factors
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Gender = circ_data$Gender,
  PrimSite = circ_data$PrimSite,
  pT = circ_data$pT,
  pN = circ_data$pN,
  Grade = circ_data$Grade,
  ACT = circ_data$ACT,
  MSI = circ_data$MSI,
  BRAF.V600E = circ_data$BRAF.V600E,
  RAS = circ_data$RAS,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.Surveillance = circ_data$ctDNA.Surveillance,
  ctDNA.anytime = circ_data$ctDNA.anytime,
  DFS.Event = circ_data$DFS.Event,
  
  col = list(Stage = c("I" = "seagreen1", "II" = "orange", "III" = "purple"),
    Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("Right-sided colon" = "brown", "Left-sided colon" ="darkgreen"),
    pT = c("T0" = "khaki","T1-T2" = "khaki", "T3-T4" ="brown2"),
    pN = c("N0" = "cornflowerblue", "N1-N2" ="orange2"),
    Grade = c("GX" = "grey","G1" = "coral", "G2" ="darkgreen", "G3" = "yellow3"),
    ACT = c("TRUE" = "#C1211A", "FALSE" ="#008BCE"),
    MSI = c("MSS" = "grey", "MSI-High" ="black"),
    BRAF.V600E = c("WT" = "grey", "MUT" ="black"),
    RAS = c("WT" = "grey", "MUT" ="black"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.Surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    DFS.Event = c("TRUE" = "red3", "FALSE" ="blue")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
```


#DFS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#ctDNA sample positive in the MRD Window - 2-10 week intervals
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
  filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD, 
                                  breaks = c(2, 4, 6, 8, 10), 
                                  right = FALSE, 
                                  labels = c("2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
  filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_stats <- filtered_data %>%
  summarise(
    total_samples = n(),
    total_positive = sum(biomarker_status == "POSITIVE"),
    total_negative = sum(biomarker_status == "NEGATIVE"),
    overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
  )

combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)

# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples at the MRD Window',
  xlab = 'Weeks from Surgery',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$MRD.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
filtered_data <- circ_data %>%
  filter(ctDNA.MRD >= 0 & ctDNA.MRD <= 10) #intervals for 0-10 weeks
filtered_data$ctDNA_bucket <- cut(filtered_data$ctDNA.MRD, 
                                  breaks = c(0, 2, 4, 6, 8, 10), 
                                  right = FALSE, 
                                  labels = c("0-2","2-4", "4-6", "6-8", "8-10"))
filtered_data <- filtered_data %>%
  filter(!is.na(ctDNA_bucket))
rate_by_bucket <- filtered_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_stats <- filtered_data %>%
  summarise(
    total_samples = n(),
    total_positive = sum(biomarker_status == "POSITIVE"),
    total_negative = sum(biomarker_status == "NEGATIVE"),
    overall_percentage_positive = mean(biomarker_status == "POSITIVE") * 100
  )

combined_results <- bind_rows(rate_by_bucket, overall_stats)
print(combined_results)

# Create the stacked bar plot for positivity and negativity rates by bucket
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples at the MRD Window',
  xlab = 'Weeks from Surgery',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)
```

#Median number of timepoints in the MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads")
filtered_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
filtered_data <- filtered_data[filtered_data$MRD.Window==TRUE,]
filtered_data <- filtered_data[!is.na(filtered_data$cfDNAconc), ]
filtered_data <- filtered_data %>%
  filter(ctDNA.MRD >= 2 & ctDNA.MRD <= 10) #intervals for 2-10 weeks

# Calculate the median number of ctDNA tests per patient
median_ctDNA_tests <- filtered_data %>%
  group_by(pts_id) %>%
  summarise(num_tests = n()) %>%
  summarise(median_tests = median(num_tests))
ctDNA_stats <- filtered_data %>%
  group_by(pts_id) %>%
  tally() %>%
  summarise(
    median_tests = median(n),
    min_tests = min(n),
    max_tests = max(n)
  )

print(median_ctDNA_tests)
print(ctDNA_stats)
```

#Multivariate cox regression for DFS at the MRD Window & Age threshold as 50 years - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group2 + PrimSite + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```


#Univariate cox regression for factors used in MVA - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Gender, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$Age.Group2 <- factor(circ_data$Age.Group2, levels = c("1", "2"), labels = c("<50", "≥50")) #univariate for Age
cox_fit <- coxph(surv_object ~ Age.Group2, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon")) #univariate for Tumor Location
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4")) #univariate for Overall T Stage
cox_fit <- coxph(surv_object ~ pT, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2")) #univariate for Overall N Stage
cox_fit <- coxph(surv_object ~ pN, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High")) #univariate for MSI
cox_fit <- coxph(surv_object ~ MSI, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E")) #univariate for BRAF
cox_fit <- coxph(surv_object ~ BRAF.V600E, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut")) #univariate for RAS
cox_fit <- coxph(surv_object ~ RAS, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA at the MRD Window - Stage II
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA at the MRD Window - Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA at the MRD Window - Stage II & Risk Groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$Risk.Group!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.II.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.Risk, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Stage.II.Risk) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage II Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Stage.II.Risk <- factor(circ_data$ctDNA.Stage.II.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxphf(surv_object ~ ctDNA.Stage.II.Risk, data=circ_data) 
summary(cox_fit)
```

#DFS by ctDNA at the MRD Window - Stage III & Risk Groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.III.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[!is.na(circ_data$ctDNA.Stage.III.Risk), ]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Stage.III.Risk) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18, 24))
circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("1","2","3","4"), labels = c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.Stage.III.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.III.Risk = case_when(
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "Low" ~ 1,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "Low" ~ 2,
    ctDNA.MRD == "NEGATIVE" & Risk.Group == "High" ~ 3,
    ctDNA.MRD == "POSITIVE" & Risk.Group == "High" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.III.Risk, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.III.Risk, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA MRD & Stage III Risk Groups", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & Low Risk", "ctDNA(+) & Low Risk", "ctDNA(-) & High Risk", "ctDNA(+) & High Risk"), legend.title="")
summary(KM_curve, times= c(18))
circ_data$ctDNA.Stage.III.Risk <- factor(circ_data$ctDNA.Stage.III.Risk, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.III.Risk, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
```

#OS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#DFS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#DFS by ctDNA at the Surveillance Window - Stages II
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#DFS by ctDNA at the Surveillance Window - Stages III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II")),]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.Event <- as.logical(circ_data$OS.Event)
circ_data$OS.months <- as.numeric(circ_data$OS.months)
circ_data$DFS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#ctDNA sample positive in the Surveillance Window - 6mo intervals
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]
circ_data$ctDNA_bucket <- cut(circ_data$ctDNA.Surv, 
                              breaks = seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6), 
                              right = FALSE, 
                              labels = paste0(seq(0, max(circ_data$ctDNA.Surv, na.rm = TRUE) - 6, by = 6), 
                                              "-", 
                                              seq(6, max(circ_data$ctDNA.Surv, na.rm = TRUE), by = 6)))

rate_by_bucket <- circ_data %>%
  group_by(ctDNA_bucket) %>%
  summarise(
    n_total = n(),  # Total number of patients in the bucket
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
overall_totals <- circ_data %>%
  summarise(
    ctDNA_bucket = "Total",  # Label for the total row
    n_total = n(),  # Total number of patients across all buckets
    n_positive = sum(biomarker_status == "POSITIVE"),  # Total number of positive cases across all buckets
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Overall positivity rate
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Total number of negative cases across all buckets
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Overall negativity rate
  )
positivity_rate_with_total <- bind_rows(rate_by_bucket, overall_totals)
print(positivity_rate_with_total)
bar_midpoints <- barplot(
  t(as.matrix(rate_by_bucket[, c("percentage_positive", "percentage_negative")])),  # Transpose to get correct format
  names.arg = rate_by_bucket$ctDNA_bucket,
  col = c("red", "blue"),  # Red for positivity and blue for negativity
  main = '% ctDNA Positive and Negative Samples by 6-month Interval',
  xlab = 'Time from Start of Surveillance (Months)',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_bucket$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_bucket$n_total + 3, labels = rate_by_bucket$n_total, col = "black", cex = 0.8)
```

#Median number of timepoints in the Surveillance Window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$Surveillance.Window == TRUE,]
circ_data <- circ_data[!is.na(circ_data$cfDNAconc), ]

# Calculate the median number of ctDNA tests per patient
ctDNA_stats <- circ_data %>%
  group_by(pts_id) %>%
  tally() %>%
  summarise(
    median_tests = median(n),
    min_tests = min(n),
    max_tests = max(n)
  )

# Print the result
print(ctDNA_stats)
```

#Time between imaging at the Surveillance window by ctDNA status
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)
median_AverageDateDifference <- aggregate(AverageDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_AverageDateDifference)
circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(AverageDateDifference~ctDNA_Surveillance, data=circ_data, main="Average Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Average Time Between Imaging (Days)", col="white",border="black")
m1<-wilcox.test(AverageDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC Imaging Frequency.csv")
tally(~ctDNA_Surveillance, data=circ_data, margins = TRUE)
median_MedianDateDifference <- aggregate(MedianDateDifference ~ ctDNA_Surveillance, data = circ_data, FUN = median)
print(median_MedianDateDifference)
circ_data$ctDNA_Surveillance <- factor(circ_data$ctDNA_Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative (n=151)","Positive (n=22)"))
boxplot(MedianDateDifference~ctDNA_Surveillance, data=circ_data, main="Median Time between Imaging at Surveillance Window", xlab="ctDNA at the Surveillance Window", ylab="Median Time Between Imaging (Days)", col="white",border="black")
m2<-wilcox.test(MedianDateDifference ~ ctDNA_Surveillance, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)
```

#Time-dependent analysis in surveillance window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
```


#Time-dependent analysis in surveillance window - all stages ACT-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="TRUE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
```


#Time-dependent analysis in surveillance window - all stages Non-ACT-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("CLIA CRC Time_Dependent_analysis.csv")
dt_final <- dt_final[dt_final$CRC.Cohort=="TRUE",]
dt_final <- dt_final[dt_final$ACT=="FALSE",]
dt_final <- dt_final[dt_final$tstart!="",]

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
```


#DFS by ctDNA subgroups at the Surveillance window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~Surveillance.Groups, data = circ_data)
event_summary <- circ_data %>%
  group_by(Surveillance.Groups) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("All Negative","Negative-Positive","All Positive"))
KM_curve <- survfit(surv_object ~ Surveillance.Groups, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","purple", "red"), title="DFS - ctDNA Subgroups Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All Negative","Negative-Positive","All Positive"), legend.title="")
summary(KM_curve, times= c(24))
cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Surveillance.Groups!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
circ_data$Surveillance.Groups <- factor(circ_data$Surveillance.Groups, levels=c("Negative-Positive","All Positive", "All Negative"))
cox_fit <- coxph(surv_object ~ Surveillance.Groups, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```

#Demographics table for ctDNA positive at the Surveillance window with no radiological recurrence
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Surveillance=="POSITIVE",]
circ_data <- circ_data[circ_data$DFS.Event==FALSE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    pT,
    pN,
    Stage,
    Grade,
    NAC,
    ACT,
    MSI,
    BRAF.V600E,
    RAS,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon")),
    pT = factor(pT, levels = c("T0","T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    Stage = factor(Stage, levels = c("I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3","GX")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF WT", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS WT", "RAS Mut")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```

#DFS by ctDNA at 12 months post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.12months!="",]
circ_data$DFS.months=circ_data$DFS.months-12
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.12months, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.12months) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.12months, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA at 12 months | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.12months <- factor(circ_data$ctDNA.12months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.12months, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA 4-weeks post-adjuvant chemotherapy
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postACT!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.postACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 4 weeks post-ACT | All pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postACT, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ACT treatment in MRD negative - High Risk Stage II or Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + MSI + Stage, data=circ_data)
summary(cox_fit)

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```


#DFS by ACT treatment in MRD positive - High Risk Stage II or Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Age.Group + Gender + Stage, data=circ_data)
summary(cox_fit)

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_fit)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_fit)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```


#DFS by ctDNA at the MRD Window & ACT - MSS Stable all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSS",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSS pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & No ACT","ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxph(surv_object ~ ctDNA.ACT, data=circ_data)
summary(cox_fit)
```


#DFS by ctDNA at the MRD Window & ACT - MSI High all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6, 24))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("3","1","2","4"), labels = c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500, maxstep = 1) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data$ctDNA.ACT <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.ACT = case_when(
    ctDNA.MRD == "NEGATIVE" & ACT == "TRUE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ACT == "TRUE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ACT == "FALSE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ACT == "FALSE" ~ 4
  ))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.ACT, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="DFS - ctDNA MRD & ACT | MSI-High pts", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ACT", "ctDNA(+) & ACT", "ctDNA(-) & No ACT", "ctDNA(+) & No ACT"), legend.title="")
summary(KM_curve, times= c(6))
circ_data$ctDNA.ACT <- factor(circ_data$ctDNA.ACT, levels=c("2","4","1","3"))
cox_fit <- coxphf(surv_object ~ ctDNA.ACT, data=circ_data, maxit = 500) #modify maxexit to reveal NA values in cox_fit
summary(cox_fit)
```


#DFS by ctDNA Dynamics from MRD to Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#DFS by ctDNA Dynamics from MRD to Surveillance Window - High Risk Stage II or Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC_Clinical Data 122023.csv")
circ_data <- circ_data[circ_data$CLIA.CRC==TRUE,]
circ_data <- circ_data[circ_data$Risk.Stage==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]

circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.Surveillance == "POSITIVE" ~ 4
  )) %>%
  filter(!is.na(ctDNA.Dynamics))

circ_data$DFS.Event <- as.logical(circ_data$DFS.Event)
circ_data$DFS.months <- as.numeric(circ_data$DFS.months)
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to Surveillance Window | High Risk Stage II or Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","4","1","2"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#Median cfDNA concentration across different windows post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
median_cfDNAconc <- circ_data %>%
  group_by(Timepoint) %>%
  dplyr::summarize(
    median_cfDNAconc = median(cfDNAconc, na.rm = TRUE),
    n_observations = n(),
    .groups = "drop"
  )
print(median_cfDNAconc)
boxplot(cfDNAconc~Timepoint, data=circ_data, main="median cfDNA Concentration", xlab="Intervals from Surgery", ylab="cfDNA Concentration", col="white",border="black", ylim = c(0, 40))

#Pairwise Wilcoxon-test p values
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)

# Initialize a matrix to store p-values for manual Wilcoxon tests
timepoints <- levels(circ_data$Timepoint)
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints

# Perform pairwise Wilcoxon tests manually and store p-values
for (i in 1:length(timepoints)) {
  for (j in i:length(timepoints)) {
    if (i != j) {
      # Subset data for the two Timepoints
      data1 <- circ_data %>% filter(Timepoint == timepoints[i]) %>% pull(cfDNAconc)
      data2 <- circ_data %>% filter(Timepoint == timepoints[j]) %>% pull(cfDNAconc)
      
      # Perform Wilcoxon test and store the p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Symmetric matrix
    } else {
      p_value_matrix[i, j] <- 1  # Set diagonal to 1 for clarity
    }
  }
}

# Replace NA values with 1 in the p-value matrix for completeness
p_value_matrix[is.na(p_value_matrix)] <- 1.00

# Melt the p-value matrix for plotting
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")

# Add asterisks based on p-value thresholds
p_value_data <- p_value_data %>%
  mutate(
    asterisk = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
  geom_tile(color = "white") +  # Background tiles for grid
  geom_text(aes(label = asterisk), color = "black", vjust = -0.5) +  # Add asterisks
  scale_fill_gradient(low = "lightgreen", high = "red") +  # Gradient for p-values
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Pairwise Wilcoxon-Test P-Values",
       fill = "P-Value")
```


#ctDNA positivity across different windows post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)

rate_by_timepoint <- circ_data %>%
  group_by(Timepoint) %>%
  summarise(
    n_total = n(),  # Total number of patients in each Timepoint
    n_positive = sum(biomarker_status == "POSITIVE"),  # Number of positive cases
    n_negative = sum(biomarker_status == "NEGATIVE"),  # Number of negative cases
    percentage_positive = mean(biomarker_status == "POSITIVE") * 100,  # Positivity rate
    percentage_negative = mean(biomarker_status == "NEGATIVE") * 100  # Negativity rate
  )
print(rate_by_timepoint)

# Create the stacked bar plot for positivity and negativity rates by Timepoint
bar_midpoints <- barplot(
  t(as.matrix(rate_by_timepoint[, c("percentage_positive", "percentage_negative")])),  # Transpose to get the correct format
  names.arg = rate_by_timepoint$Timepoint,
  col = c("red", "blue"),  # Colors: red for positive, blue for negative
  main = '% ctDNA Positive and Negative Samples by Timepoint',
  xlab = 'Timepoint',
  ylab = '% ctDNA Samples',
  ylim = c(0, 100),
  legend = c("% Positive", "% Negative"),  # Adding a legend for clarification
  args.legend = list(x = "topright")
)
par(new = TRUE)
plot(bar_midpoints, rate_by_timepoint$n_total, type = "b", col = "black", pch = 19, axes = FALSE, xlab = "", ylab = "", lwd = 2)
axis(side = 4)  # Add the secondary y-axis on the right
mtext("Total Number of Samples", side = 4, line = 3)  # Label for the secondary y-axis
text(bar_midpoints, rate_by_timepoint$n_total + 3, labels = rate_by_timepoint$n_total, col = "black", cex = 0.8)

#Perform fisher's exact test matrix and heatmap
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("0-2 weeks", "2-4 weeks", "4-6 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
timepoints <- levels(circ_data$Timepoint)  # Use ordered levels
p_value_matrix <- matrix(NA, nrow = length(timepoints), ncol = length(timepoints))
rownames(p_value_matrix) <- timepoints
colnames(p_value_matrix) <- timepoints

for (i in 1:length(timepoints)) {
  for (j in i:length(timepoints)) {
    if (i != j) {
      # Subset data for the two Timepoints
      subset1 <- circ_data %>% filter(Timepoint == timepoints[i])
      subset2 <- circ_data %>% filter(Timepoint == timepoints[j])
      
      # Create contingency table
      contingency_table <- matrix(c(
        sum(subset1$biomarker_status == "POSITIVE"), sum(subset1$biomarker_status == "NEGATIVE"),
        sum(subset2$biomarker_status == "POSITIVE"), sum(subset2$biomarker_status == "NEGATIVE")
      ), nrow = 2, byrow = TRUE)
      
      # Perform Fisher's exact test
      test_result <- fisher.test(contingency_table)
      
      # Store p-value in the matrix
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Symmetric matrix
    } else {
      p_value_matrix[i, j] <- 1  # Set diagonal to 1 for clarity
    }
  }
}

print(p_value_matrix)
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("Timepoint1", "Timepoint2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    asterisk = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

# Create the heatmap with asterisks for significance levels
ggplot(p_value_data, aes(x = Timepoint1, y = Timepoint2, fill = p_value)) +
  geom_tile(color = "white") +  # Background tiles for grid
  geom_text(aes(label = asterisk), color = "black", vjust = -0.5) +  # Add asterisks
  scale_fill_gradient(low = "lightgreen", high = "red") +  # Gradient for p-values
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Pairwise Fisher's Exact Test P-Values",
       x = "Timepoint 1",
       y = "Timepoint 2",
       fill = "P-Value")
```


#Logistic regression for ctDNA positivity
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA CRC cfDNA data_ctDNA timepoints.csv")
circ_data <- circ_data[circ_data$cfDNA.Include == TRUE,]

# Calculate the 20th, 40th, 60th, 80th, and 100th percentiles for cfDNAconc
thresholds <- quantile(circ_data$cfDNAconc, probs = seq(0, 1, 0.2), na.rm = TRUE)
print(thresholds)
circ_data <- circ_data %>%
  mutate(cfDNAlevels = cut(cfDNAconc, 
                           breaks = thresholds, 
                           include.lowest = TRUE, 
                           labels = c("Q1", "Q2", "Q3", "Q4", "Q5")))

circ_data$cfDNAconc <- as.numeric(circ_data$cfDNAconc)
circ_data$biomarker_status <- factor(circ_data$biomarker_status, levels = c("NEGATIVE", "POSITIVE"))
circ_data$cfDNAlevels <- factor(circ_data$cfDNAlevels, levels = c("Q3", "Q1", "Q2", "Q4", "Q5"), labels = c("4.76-6.24 ng/mL (q3)", "<3.57 ng/mL (q1)", "3.57-4.76 ng/mL (q2)", "6.24-9.36 ng/mL (q4)", ">9.36 ng/mL (q5)"))
circ_data$Timepoint <- factor(circ_data$Timepoint, levels = c("4-6 weeks", "0-2 weeks", "2-4 weeks", "6-8 weeks", "8-10 weeks","On-treatment", "Surveillance"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("I", "II", "III"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("WT", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("WT", "Mut"))
logistic_model <- glm(biomarker_status ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS,
                      data = circ_data,
                      family = binomial)

# Extract odds ratios and confidence intervals
results <- tidy(logistic_model, conf.int = TRUE, exponentiate = TRUE) %>%
  mutate(term = factor(term, levels = term))  # Preserve term ordering
ggplot(results, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange() +
  geom_hline(yintercept = 1, linetype = "dashed", color = "gray") +  # Reference line at OR = 1
  coord_flip() +
  labs(title = "Odds Ratios for Biomarker Status (Forest Plot)",
       x = "Predictor",
       y = "Odds Ratio (95% CI)") +
  theme_minimal()

cox_model <- coxph(Surv(rep(1, nrow(circ_data)), biomarker_status == "POSITIVE") ~ cfDNAlevels + Timepoint + Stage + MSI + BRAF.V600E + RAS, 
                   data = circ_data)
ggforest(cox_model, data = circ_data, main = "Odds Ratios for Biomarker Status", cpositions = c(0.02, 0.22, 0.4))

# Adjust p-values using False Discovery Rate (FDR) adjustment (Benjamini-Hochberg method)
p_values <- summary(cox_model)$coefficients[, 5]
adjusted_p_values <- p.adjust(p_values, method = "fdr")
results <- data.frame(
  Variable = rownames(summary(cox_model)$coefficients),
  Original_P_Value = p_values,
  FDR_Adjusted_P_Value = adjusted_p_values
)
print(results)
```

